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Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses

Author

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  • Kevin Leahy

    (School of Engineering, University College Cork, Cork T12 K8AF, Ireland
    Science Foundation Ireland (SFI) MaREI Centre, Environmental Research Institute, University College Cork, Cork P43 C573, Ireland)

  • Colm Gallagher

    (School of Engineering, University College Cork, Cork T12 K8AF, Ireland
    Science Foundation Ireland (SFI) MaREI Centre, Environmental Research Institute, University College Cork, Cork P43 C573, Ireland)

  • Peter O’Donovan

    (School of Engineering, University College Cork, Cork T12 K8AF, Ireland)

  • Dominic T. J. O’Sullivan

    (School of Engineering, University College Cork, Cork T12 K8AF, Ireland)

Abstract

In order to remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. However, thus far, wind turbine reliability information has been closely guarded by the original equipment manufacturers (OEMs), and turbine reliability studies often rely on data that are not always in a usable or consistent format. In addition, issues with turbine maintenance logs and alarm system data can make it hard to identify historical periods of faulty operation. This means that building new and effective data-driven condition monitoring techniques and methods can be challenging, especially those that rely on supervisory control and data acquisition (SCADA) system data. Such data are rarely standardised, resulting in challenges for researchers in contextualising these data. This work aims to summarise some of the issues seen in previous studies, highlighting the common problems seen by researchers working in the areas of condition monitoring and reliability analysis. Standards and policy initiatives that aim to alleviate some of these problems are given, and a summary of their recommendations is presented. The main finding from this work is that industry would benefit hugely from unified standards for turbine taxonomies, alarm codes, SCADA operational data and maintenance and fault reporting.

Suggested Citation

  • Kevin Leahy & Colm Gallagher & Peter O’Donovan & Dominic T. J. O’Sullivan, 2019. "Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses," Energies, MDPI, vol. 12(2), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:201-:d:196145
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    References listed on IDEAS

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    Cited by:

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    3. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
    4. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
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    12. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    13. Juan Izquierdo & Adolfo Crespo Márquez & Jone Uribetxebarria & Asier Erguido, 2019. "Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance," Energies, MDPI, vol. 12(11), pages 1-17, May.
    14. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.
    15. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    16. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    17. Xiangqing Yin & Yi Liu & Li Yang & Wenchao Gao, 2022. "Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting," Energies, MDPI, vol. 15(17), pages 1-22, August.
    18. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Methodologies in Smart Grid-Integrated Doubly Fed Induction Generator Design Optimization and Reliability Assessment: A Review," Energies, MDPI, vol. 15(19), pages 1-39, September.

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